Unsupervised Alignment for Segmental-based Language Understanding

Abstract : Recent years' most efficient approaches for language understanding are statistical. These approaches benefit from a segmental semantic annotation of corpora. To reduce the production cost of such corpora, this paper proposes a method that is able to match first identified concepts with word sequences in an unsuper-vised way. This method based on automatic alignment is used by an understanding system based on conditional random fields and is evaluated on a spoken dialogue task using either manual or automatic transcripts.
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01317563
Contributor : Bibliothèque Universitaire Déposants Hal-Avignon <>
Submitted on : Wednesday, February 27, 2019 - 10:38:28 AM
Last modification on : Wednesday, May 15, 2019 - 10:12:03 AM
Long-term archiving on : Tuesday, May 28, 2019 - 1:19:08 PM

File

UNSUP11a.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01317563, version 1

Collections

Citation

Stéphane Huet, Fabrice Lefèvre. Unsupervised Alignment for Segmental-based Language Understanding. EMNLP Workshop on Unsupervised Learning in NLP (UNSUP), Aug 2011, Edinburgh, United Kingdom. pp.97-104. ⟨hal-01317563⟩

Share

Metrics

Record views

56

Files downloads

4